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Reseach Article

Distribution System Loss Reduction through Hybrid Heuristic Technique

by John Wiselin, Perumal Shankar S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 6
Year of Publication: 2012
Authors: John Wiselin, Perumal Shankar S
10.5120/5543-7603

John Wiselin, Perumal Shankar S . Distribution System Loss Reduction through Hybrid Heuristic Technique. International Journal of Computer Applications. 41, 6 ( March 2012), 11-17. DOI=10.5120/5543-7603

@article{ 10.5120/5543-7603,
author = { John Wiselin, Perumal Shankar S },
title = { Distribution System Loss Reduction through Hybrid Heuristic Technique },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 6 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number6/5543-7603/ },
doi = { 10.5120/5543-7603 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:53.546234+05:30
%A John Wiselin
%A Perumal Shankar S
%T Distribution System Loss Reduction through Hybrid Heuristic Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 6
%P 11-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a hybrid technology to solve the distribution system reconfiguration problem. The technology with the mixture of Plant Growth Simulation Algorithm (PGSA), Greedy and heuristic based fuzzy operation has been proposed. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy with PGSA improves the efficiency of optimization by identifying the best loop sequence. Furthermore, the heuristic fuzzy has been introduced with PGSA and Greedy for handling constraints amid optimization. With the use of proposed algorithm, the system loss has been reduced convincingly without compromising the power flow constraints. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE 33 bus distribution system and 83 bus Distribution system of Taiwan Power Company.

References
  1. S. Civanlar, J. J. Grainger, H. Yin, and S. S. H. Lee, "Distribution feeder reconfiguration for loss reduction," IEEE Trans. Power Del. , vol. 3, no. 3, pp. 1217–1223, Jul. 1988.
  2. Baran ME and Wu FF, 'Network reconfiguration in distribution systems for loss reduction and load balancing," IEEE Trans. Power Del. , vol. 4, no. 1, pp. 401-1407, Jan. 1989.
  3. Aoki K, Kawabara H, and Satoh. M, "An efficient algorithm for load balancing of transformers and feeders," IEEE Trans. Power Del. , vol. 3, no. 4, pp. 1865-1872, Jul. 1988.
  4. D. Shirmohammadi and H. W. Hong, "Reconfiguration of electric distribution networks for resistive line losses reduction," IEEE Trans. Power Del. , vol. 4, no. 2, pp. 1492–1498, Apr. 1989.
  5. Goswami . S. K and Basu . S. K (1992), "A new algorithm for the reconfiguration of distribution feeders for loss minimization", IEEE Trans. Power Del. ,Vol. 7, No. 3, pp. 1484–1490.
  6. Ying-Yi . H and Saw-Yu . H (2006), "Determination of network configuration considering multiobjective in distribution systems using genetic algorithms", IEEE Trans. on Power Sys. , Vol. 20, No. 2, pp. 1062-1069.
  7. L. Whei-Min and C. Hong-Chan, "A new approach for distribution feeder reconfiguration for loss reduction and service restoration," IEEE Trans. Power Del. , vol. 13, no. 3, pp. 870–875, Jul. 1998.
  8. H. Kim, Y. Ko, and K. H. Jung, "Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems," IEEE Trans. Power Del. , vol. 8, no. 3, pp. 1356–1366, Jul. 1993.
  9. H. Salazar, R. Gallego, and R. Romero, "Artificial neural networks and clustering techniques applied in the reconfiguration of distribution sys- tems," IEEE Trans. Power Del. , vol. 21, no. 3, pp. 1735–1742, Jul. 2006.
  10. Liu CC, Lee SJ, Venkata SS, "An expert system operational aid for restoration and loss reduction of distribution systems," IEEE Trans. Power Del. , vol. 3, no. 3, pp. 619-25, Jan. 1988.
  11. B. Venkatesh, R. Ranjan, and H. B. Gooi, "Optimal reconfiguration of radial distribution systems to maximize loadability," IEEE Trans. Power Syst. , vol. 19, no. 1, pp. 260–266, Feb. 2004.
  12. K. Huang and H. Chin, "Distribution feeder energy conservation by using heuristics fuzzy approach," Electrical Power and Energy Systems, vol. 24,pp. 439-445, 2002.
  13. C. Wang and H. Z. Cheng "Optimization of Network configuration in Large distribution systems using plant growth simulation algorithm," IEEE Trans. Power Syst. , vol. 23, No. 1, pp. 119-126, Feb. 2008.
  14. S. Thiruvenkadam, A. Nirmalkumar, & M. Sathishkumar, "Distribution network optimization through fusion technology", Australian Journal of Electrical and Electronics Engg. vol. 7, no. 2, pp. 145-152, 2010.
  15. Y. H. Song, G. S. Wang, A. T. Johns, and P. Y. Wang, "Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming," Proc. Inst. Elect. Eng. , Gen. , Transm. , Distrib. , vol. 144, no. 4, pp. 345–350, Jul. 1997.
  16. A. C. B. Delbem, A. C. P. L. F. Carvalho, and N. G. Bretas, "Main chain representation for evolutionary algorithms applied to distribution system reconfiguration," IEEE Trans. Power Syst. , vol. 20, no. 1, pp. 425–436, Feb. 2005.
  17. A. C. Neto, A. B. Rodrigues, R. B. Prada, and M. G. Silva, "External Equivalent for electric power distribution networks with radial topology," IEEE Trans. Power Syst. , vol. 23, no. 3, pp. 889-895, Aug. 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Distribution Network Heuristic Fuzzy Greedy Pgsa Reconfiguration Restoration